CVE-2024-22421
Published: 19 January 2024
Summary
CVE-2024-22421 is a high-severity Relative Path Traversal (CWE-23) vulnerability in Jupyter Jupyterlab. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Credential Access (T1212); ranked at the 33.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0239
Vulnerability details
JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Users of JupyterLab who click on a malicious link may get their `Authorization` and `XSRFToken` tokens exposed to a third party when running…
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an older `jupyter-server` version. JupyterLab versions 4.1.0b2, 4.0.11, and 3.6.7 are patched. No workaround has been identified, however users should ensure to upgrade `jupyter-server` to version 2.7.2 or newer which includes a redirect vulnerability fix.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CVE-2024-22421 is a redirect vulnerability in jupyter-server used by JupyterLab that exposes Authorization and XSRFToken to third parties when users click malicious links, enabling exploitation for credential access (T1212) and stealing application access tokens (T1528).
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.